Mastering Neural Networks From Basics To Advanced Deep Learning
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Mastering Neural Networks From Basics To Advanced Deep Learning
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Author : MADHURI AMIT SAHU.
language : en
Publisher:
Release Date : 2024
Mastering Neural Networks From Basics To Advanced Deep Learning written by MADHURI AMIT SAHU. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Mastering Pytorch
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Author : Ashish Ranjan Jha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-12
Mastering Pytorch written by Ashish Ranjan Jha and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-12 with Computers categories.
Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more Book DescriptionDeep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text and music generating models using PyTorch Build a deep Q-network (DQN) model in PyTorch Export universal PyTorch models using Open Neural Network Exchange (ONNX) Become well-versed with rapid prototyping using PyTorch with fast.ai Perform neural architecture search effectively using AutoML Easily interpret machine learning (ML) models written in PyTorch using Captum Design ResNets, LSTMs, Transformers, and more using PyTorch Find out how to use PyTorch for distributed training using the torch.distributed API Who this book is for This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.
Deep Learning With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2016-05-13
Deep Learning With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-13 with Computers categories.
Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.
Mastering Neural Networks
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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date :
Mastering Neural Networks written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Unleash the Power of Deep Learning for Intelligent Systems In the realm of artificial intelligence and machine learning, neural networks stand as the driving force behind intelligent systems that mimic human cognition. "Mastering Neural Networks" is your ultimate guide to comprehending and harnessing the potential of these powerful algorithms, empowering you to create intelligent solutions that push the boundaries of innovation. About the Book: As technology advances, the capabilities of neural networks become more integral to various fields. "Mastering Neural Networks" offers an in-depth exploration of this cutting-edge subject—an essential toolkit for data scientists, engineers, and enthusiasts. This book caters to both newcomers and experienced learners aiming to excel in neural network concepts, architectures, and applications. Key Features: Neural Network Fundamentals: Begin by understanding the core principles of neural networks. Learn about artificial neurons, activation functions, and the architecture of these powerful algorithms. Feedforward Neural Networks: Dive into feedforward neural networks. Explore techniques for designing, training, and optimizing networks for various tasks. Convolutional Neural Networks: Grasp the art of convolutional neural networks. Understand how these architectures excel in image and pattern recognition tasks. Recurrent Neural Networks: Explore recurrent neural networks. Learn how to process sequences and time-series data, making them suitable for tasks like language modeling and speech recognition. Generative Adversarial Networks: Understand the significance of generative adversarial networks. Explore how these networks enable the generation of realistic images, text, and data. Transfer Learning and Fine-Tuning: Delve into transfer learning. Learn how to leverage pretrained models and adapt them to new tasks, saving time and resources. Neural Network Optimization: Grasp optimization techniques. Explore methods for improving network performance, reducing overfitting, and tuning hyperparameters. Real-World Applications: Gain insights into how neural networks are applied across industries. From healthcare to finance, discover the diverse applications of these algorithms. Why This Book Matters: In a world driven by intelligent systems, mastering neural networks offers a competitive advantage. "Mastering Neural Networks" empowers data scientists, engineers, and technology enthusiasts to leverage these cutting-edge algorithms, enabling them to create intelligent solutions that redefine the boundaries of innovation. Unleash the Future of Intelligence: In the landscape of artificial intelligence, neural networks are reshaping technology and innovation. "Mastering Neural Networks" equips you with the knowledge needed to leverage these powerful algorithms, enabling you to create intelligent solutions that push the boundaries of innovation and redefine what's possible. Whether you're a seasoned practitioner or new to the world of neural networks, this book will guide you in building a solid foundation for effective AI-driven solutions. Your journey to mastering neural networks starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Master Machine Learning Algorithms
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2016-03-04
Master Machine Learning Algorithms written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-04 with Computers categories.
You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.
Mastering Machine Learning Algorithms
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Author : Giuseppe Bonaccorso
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-05-25
Mastering Machine Learning Algorithms written by Giuseppe Bonaccorso and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-25 with Computers categories.
Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who this book is for This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.
Fundamentals Of Deep Learning
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Author : Nikhil Buduma
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-05-25
Fundamentals Of Deep Learning written by Nikhil Buduma and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-25 with Computers categories.
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Learn the fundamentals of reinforcement learning
Mastering Neural Network Computer Vision With Tensorflow And Keras
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Author : Jean Anoma
language : en
Publisher: BPB Publications
Release Date : 2025-01-06
Mastering Neural Network Computer Vision With Tensorflow And Keras written by Jean Anoma and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-06 with Computers categories.
DESCRIPTION Mastering Neural Network Computer Vision with TensorFlow and Keras provides a comprehensive guide to using TensorFlow and Keras for computer vision applications. The book enables readers to develop and exercise the skills needed to use sophisticated pre-trained computer vision models, build simple and more advanced neural network models, and optimize their performance. The different chapters of the book cover a comprehensive range of topics in computer vision and deep learning. The first chapter provides a theoretical introduction to computer vision and deep learning, and the second one provides an overview of TensorFlow and its capabilities. The subsequent chapters cover specific applications of neural networks in computer vision, such as image classification, image segmentation, and object detection, and how to tap into the power of transfer learning and pre-trained models to address those use cases. Finally, the remaining chapters cover how to design your own neural network, gather a proper dataset and train your model efficiently. They also cover image generation and ethical considerations around computer vision. By the end of this book, readers will have a strong understanding of the principles of deep learning and computer vision, as well as the skills needed to build advanced neural network models using TensorFlow. KEY FEATURES ● Master computer vision fundamentals through hands-on implementation with Tensorflow, from basics to advanced applications. ● Learn real-world techniques for preparing data, training models, and deploying computer vision solutions at scale. ● Explore state-of-the-art techniques, including transfer learning, generative models, and advanced vision tasks through practical projects. WHAT YOU WILL LEARN ● Understand essential deep learning concepts and architectures specifically designed for modern computer vision applications. ● Build practical expertise with Tensorflow and Keras while implementing pre-trained models for vision tasks. ● Learn to fine-tune existing models and design new architectures for specific vision challenges. ● Master techniques to improve model efficiency, training speed, and overall performance in real applications. ● They will know how diffusion-based models work and how to use some of the most popular ones, like DALL-E or Stable Diffusion. WHO THIS BOOK IS FOR This book is for current or aspiring deep tech professionals, students, and anyone who wishes to understand the rewarding field of computer vision. More specifically, it will also have a great impact on computer vision engineers, robotics, image processing, and video processing engineers who are willing to learn how to use neural networks to boost their performance and results. TABLE OF CONTENTS 1. Introduction to Neural Networks and Deep Learning 2. Introduction to TensorFlow and Keras 3. Presentation of Some Computer Vision Tasks and Related Dataset Structure 4. The Secret to a Great Model: A Great Dataset 5. Transfer Learning with TensorFlow and Keras 6. Segmentation with Neural Networks 7. Object Detection with Neural Networks 8. Using Pre-trained Models for Text Detection and Recognition 9. Using Pre-trained Models for Image Enhancement 10. Building Your Own Model with Keras 11. Training Your Own Model with Keras 12. Explainability of Results 13. Generative Models 14. Conclusion and Future Directions
Math For Deep Learning
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Author : Ronald T. Kneusel
language : en
Publisher: No Starch Press
Release Date : 2021-12-07
Math For Deep Learning written by Ronald T. Kneusel and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-07 with Computers categories.
Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.